Overview

Dataset statistics

Number of variables31
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory248.0 B

Variable types

Numeric17
Categorical14

Alerts

Card_Category is highly imbalanced (79.2%)Imbalance
Card_Category_num is highly imbalanced (79.2%)Imbalance
Marital_Status_Divorced is highly imbalanced (61.8%)Imbalance
CLIENTNUM has unique valuesUnique
Dependent_count has 904 (8.9%) zerosZeros
Contacts_Count_12_mon has 399 (3.9%) zerosZeros
Total_Revolving_Bal has 2470 (24.4%) zerosZeros
Avg_Utilization_Ratio has 2470 (24.4%) zerosZeros
Education_Level_num has 1500 (14.8%) zerosZeros

Reproduction

Analysis started2023-12-26 15:01:34.830691
Analysis finished2023-12-26 15:01:47.224255
Duration12.39 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3917761 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:47.273045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0912039 × 108
Q17.1303677 × 108
median7.1792636 × 108
Q37.7314353 × 108
95-th percentile8.1421203 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)60106762

Descriptive statistics

Standard deviation36903783
Coefficient of variation (CV)0.049925462
Kurtosis-0.6156397
Mean7.3917761 × 108
Median Absolute Deviation (MAD)6347700
Skewness0.99560101
Sum7.4856516 × 1012
Variance1.3618892 × 1015
MonotonicityNot monotonic
2023-12-26T10:01:47.327605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768805383 1
 
< 0.1%
711784908 1
 
< 0.1%
720133908 1
 
< 0.1%
803197833 1
 
< 0.1%
812222208 1
 
< 0.1%
757634583 1
 
< 0.1%
719362458 1
 
< 0.1%
789331908 1
 
< 0.1%
715616358 1
 
< 0.1%
806900508 1
 
< 0.1%
Other values (10117) 10117
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708095133 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828291858 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Existing Customer
8500 
Attrited Customer
1627 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters172159
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExisting Customer
2nd rowExisting Customer
3rd rowExisting Customer
4th rowExisting Customer
5th rowExisting Customer

Common Values

ValueCountFrequency (%)
Existing Customer 8500
83.9%
Attrited Customer 1627
 
16.1%

Length

2023-12-26T10:01:47.373533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:47.410529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
customer 10127
50.0%
existing 8500
42.0%
attrited 1627
 
8.0%

Most occurring characters

ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141778
82.4%
Uppercase Letter 20254
 
11.8%
Space Separator 10127
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 23508
16.6%
i 18627
13.1%
s 18627
13.1%
e 11754
8.3%
r 11754
8.3%
u 10127
7.1%
o 10127
7.1%
m 10127
7.1%
x 8500
 
6.0%
n 8500
 
6.0%
Other values (2) 10127
7.1%
Uppercase Letter
ValueCountFrequency (%)
C 10127
50.0%
E 8500
42.0%
A 1627
 
8.0%
Space Separator
ValueCountFrequency (%)
10127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162032
94.1%
Common 10127
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 23508
14.5%
i 18627
11.5%
s 18627
11.5%
e 11754
7.3%
r 11754
7.3%
C 10127
 
6.2%
u 10127
 
6.2%
o 10127
 
6.2%
m 10127
 
6.2%
E 8500
 
5.2%
Other values (5) 28754
17.7%
Common
ValueCountFrequency (%)
10127
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 23508
13.7%
i 18627
10.8%
s 18627
10.8%
e 11754
 
6.8%
r 11754
 
6.8%
10127
 
5.9%
C 10127
 
5.9%
u 10127
 
5.9%
o 10127
 
5.9%
m 10127
 
5.9%
Other values (6) 37254
21.6%

Customer_Age
Real number (ℝ)

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.32596
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:47.451858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814
Coefficient of variation (CV)0.1730523
Kurtosis-0.28861992
Mean46.32596
Median Absolute Deviation (MAD)6
Skewness-0.033605016
Sum469143
Variance64.269307
MonotonicityNot monotonic
2023-12-26T10:01:47.600833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 500
 
4.9%
49 495
 
4.9%
46 490
 
4.8%
45 486
 
4.8%
47 479
 
4.7%
43 473
 
4.7%
48 472
 
4.7%
50 452
 
4.5%
42 426
 
4.2%
51 398
 
3.9%
Other values (35) 5456
53.9%
ValueCountFrequency (%)
26 78
0.8%
27 32
 
0.3%
28 29
 
0.3%
29 56
 
0.6%
30 70
 
0.7%
31 91
0.9%
32 106
1.0%
33 127
1.3%
34 146
1.4%
35 184
1.8%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 101
1.0%
64 43
0.4%
63 65
0.6%
62 93
0.9%
61 93
0.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Length

2023-12-26T10:01:47.643626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:47.677114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
f 5358
52.9%
m 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10127
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 10127
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Dependent_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:47.708823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2023-12-26T10:01:47.745341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%

Education_Level
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Graduate
3696 
High School
2602 
Uneducated
1500 
College
1016 
Post-Graduate
713 

Length

Max length13
Median length11
Mean length9.3779994
Min length7

Characters and Unicode

Total characters94971
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowGraduate
4th rowHigh School
5th rowUneducated

Common Values

ValueCountFrequency (%)
Graduate 3696
36.5%
High School 2602
25.7%
Uneducated 1500
14.8%
College 1016
 
10.0%
Post-Graduate 713
 
7.0%
Doctorate 600
 
5.9%

Length

2023-12-26T10:01:47.790040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:47.831568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate 3696
29.0%
high 2602
20.4%
school 2602
20.4%
uneducated 1500
11.8%
college 1016
 
8.0%
post-graduate 713
 
5.6%
doctorate 600
 
4.7%

Most occurring characters

ValueCountFrequency (%)
a 10918
11.5%
e 10041
 
10.6%
o 8133
 
8.6%
t 7822
 
8.2%
d 7409
 
7.8%
u 5909
 
6.2%
h 5204
 
5.5%
r 5009
 
5.3%
c 4702
 
5.0%
l 4634
 
4.9%
Other values (13) 25190
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 78214
82.4%
Uppercase Letter 13442
 
14.2%
Space Separator 2602
 
2.7%
Dash Punctuation 713
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10918
14.0%
e 10041
12.8%
o 8133
10.4%
t 7822
10.0%
d 7409
9.5%
u 5909
7.6%
h 5204
6.7%
r 5009
6.4%
c 4702
6.0%
l 4634
5.9%
Other values (4) 8433
10.8%
Uppercase Letter
ValueCountFrequency (%)
G 4409
32.8%
S 2602
19.4%
H 2602
19.4%
U 1500
 
11.2%
C 1016
 
7.6%
P 713
 
5.3%
D 600
 
4.5%
Space Separator
ValueCountFrequency (%)
2602
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 713
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91656
96.5%
Common 3315
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10918
11.9%
e 10041
11.0%
o 8133
 
8.9%
t 7822
 
8.5%
d 7409
 
8.1%
u 5909
 
6.4%
h 5204
 
5.7%
r 5009
 
5.5%
c 4702
 
5.1%
l 4634
 
5.1%
Other values (11) 21875
23.9%
Common
ValueCountFrequency (%)
2602
78.5%
- 713
 
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10918
11.5%
e 10041
 
10.6%
o 8133
 
8.6%
t 7822
 
8.2%
d 7409
 
7.8%
u 5909
 
6.2%
h 5204
 
5.5%
r 5009
 
5.3%
c 4702
 
5.0%
l 4634
 
4.9%
Other values (13) 25190
26.5%

Marital_Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Married
5194 
Single
4181 
Divorced
752 

Length

Max length8
Median length7
Mean length6.6614002
Min length6

Characters and Unicode

Total characters67460
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 5194
51.3%
Single 4181
41.3%
Divorced 752
 
7.4%

Length

2023-12-26T10:01:47.882360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:47.921294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
married 5194
51.3%
single 4181
41.3%
divorced 752
 
7.4%

Most occurring characters

ValueCountFrequency (%)
r 11140
16.5%
i 10127
15.0%
e 10127
15.0%
d 5946
8.8%
M 5194
7.7%
a 5194
7.7%
S 4181
 
6.2%
n 4181
 
6.2%
g 4181
 
6.2%
l 4181
 
6.2%
Other values (4) 3008
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57333
85.0%
Uppercase Letter 10127
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 11140
19.4%
i 10127
17.7%
e 10127
17.7%
d 5946
10.4%
a 5194
9.1%
n 4181
 
7.3%
g 4181
 
7.3%
l 4181
 
7.3%
v 752
 
1.3%
o 752
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
M 5194
51.3%
S 4181
41.3%
D 752
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 67460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 11140
16.5%
i 10127
15.0%
e 10127
15.0%
d 5946
8.8%
M 5194
7.7%
a 5194
7.7%
S 4181
 
6.2%
n 4181
 
6.2%
g 4181
 
6.2%
l 4181
 
6.2%
Other values (4) 3008
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 11140
16.5%
i 10127
15.0%
e 10127
15.0%
d 5946
8.8%
M 5194
7.7%
a 5194
7.7%
S 4181
 
6.2%
n 4181
 
6.2%
g 4181
 
6.2%
l 4181
 
6.2%
Other values (4) 3008
 
4.5%

Income_Category
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Less than $40K
3732 
$80K - $120K
2014 
$40K - $60K
1878 
$60K - $80K
1770 
$120K +
733 

Length

Max length14
Median length12
Mean length12.014911
Min length7

Characters and Unicode

Total characters121675
Distinct characters18
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$60K - $80K
2nd rowLess than $40K
3rd row$80K - $120K
4th rowLess than $40K
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
Less than $40K 3732
36.9%
$80K - $120K 2014
19.9%
$40K - $60K 1878
18.5%
$60K - $80K 1770
17.5%
$120K + 733
 
7.2%

Length

2023-12-26T10:01:47.964208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:48.011134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
6395
21.6%
40k 5610
18.9%
80k 3784
12.8%
less 3732
12.6%
than 3732
12.6%
60k 3648
12.3%
120k 2747
9.3%

Most occurring characters

ValueCountFrequency (%)
19521
16.0%
K 15789
13.0%
0 15789
13.0%
$ 15789
13.0%
s 7464
 
6.1%
- 5662
 
4.7%
4 5610
 
4.6%
8 3784
 
3.1%
e 3732
 
3.1%
L 3732
 
3.1%
Other values (8) 24803
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34325
28.2%
Lowercase Letter 26124
21.5%
Space Separator 19521
16.0%
Uppercase Letter 19521
16.0%
Currency Symbol 15789
13.0%
Dash Punctuation 5662
 
4.7%
Math Symbol 733
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15789
46.0%
4 5610
 
16.3%
8 3784
 
11.0%
6 3648
 
10.6%
1 2747
 
8.0%
2 2747
 
8.0%
Lowercase Letter
ValueCountFrequency (%)
s 7464
28.6%
e 3732
14.3%
n 3732
14.3%
a 3732
14.3%
h 3732
14.3%
t 3732
14.3%
Uppercase Letter
ValueCountFrequency (%)
K 15789
80.9%
L 3732
 
19.1%
Space Separator
ValueCountFrequency (%)
19521
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 15789
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5662
100.0%
Math Symbol
ValueCountFrequency (%)
+ 733
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76030
62.5%
Latin 45645
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
19521
25.7%
0 15789
20.8%
$ 15789
20.8%
- 5662
 
7.4%
4 5610
 
7.4%
8 3784
 
5.0%
6 3648
 
4.8%
1 2747
 
3.6%
2 2747
 
3.6%
+ 733
 
1.0%
Latin
ValueCountFrequency (%)
K 15789
34.6%
s 7464
16.4%
e 3732
 
8.2%
L 3732
 
8.2%
n 3732
 
8.2%
a 3732
 
8.2%
h 3732
 
8.2%
t 3732
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19521
16.0%
K 15789
13.0%
0 15789
13.0%
$ 15789
13.0%
s 7464
 
6.1%
- 5662
 
4.7%
4 5610
 
4.6%
8 3784
 
3.1%
e 3732
 
3.1%
L 3732
 
3.1%
Other values (8) 24803
20.4%

Card_Category
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Blue
9436 
Silver
 
555
Gold
 
116
Platinum
 
20

Length

Max length8
Median length4
Mean length4.1175077
Min length4

Characters and Unicode

Total characters41698
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 9436
93.2%
Silver 555
 
5.5%
Gold 116
 
1.1%
Platinum 20
 
0.2%

Length

2023-12-26T10:01:48.066212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:48.105706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
blue 9436
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31571
75.7%
Uppercase Letter 10127
 
24.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 10127
32.1%
e 9991
31.6%
u 9456
30.0%
i 575
 
1.8%
v 555
 
1.8%
r 555
 
1.8%
o 116
 
0.4%
d 116
 
0.4%
a 20
 
0.1%
t 20
 
0.1%
Other values (2) 40
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 9436
93.2%
S 555
 
5.5%
G 116
 
1.1%
P 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 41698
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Months_on_book
Real number (ℝ)

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.148352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2023-12-26T10:01:48.194125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8125802
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.232878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5544079
Coefficient of variation (CV)0.40770496
Kurtosis-1.0061305
Mean3.8125802
Median Absolute Deviation (MAD)1
Skewness-0.16245241
Sum38610
Variance2.4161838
MonotonicityNot monotonic
2023-12-26T10:01:48.269342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
2 1243
12.3%
1 910
 
9.0%
ValueCountFrequency (%)
1 910
 
9.0%
2 1243
12.3%
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
ValueCountFrequency (%)
6 1866
18.4%
5 1891
18.7%
4 1912
18.9%
3 2305
22.8%
2 1243
12.3%
1 910
 
9.0%

Months_Inactive_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.303123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2023-12-26T10:01:48.339561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%

Contacts_Count_12_mon
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.374397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2023-12-26T10:01:48.412180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

Credit_Limit
Real number (ℝ)

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.9537
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.458129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.7767
Coefficient of variation (CV)1.0529223
Kurtosis1.8089893
Mean8631.9537
Median Absolute Deviation (MAD)2593
Skewness1.6667258
Sum87415795
Variance82605861
MonotonicityNot monotonic
2023-12-26T10:01:48.511253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516 508
 
5.0%
1438.3 507
 
5.0%
9959 18
 
0.2%
15987 18
 
0.2%
23981 12
 
0.1%
2490 11
 
0.1%
6224 11
 
0.1%
3735 11
 
0.1%
7469 10
 
0.1%
2069 8
 
0.1%
Other values (6195) 9013
89.0%
ValueCountFrequency (%)
1438.3 507
5.0%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 2
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 2
 
< 0.1%
1451 2
 
< 0.1%
1452 2
 
< 0.1%
ValueCountFrequency (%)
34516 508
5.0%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34140 1
 
< 0.1%
34058 1
 
< 0.1%
34010 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

ZEROS 

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.8141
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.564119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.98734
Coefficient of variation (CV)0.70087503
Kurtosis-1.1459918
Mean1162.8141
Median Absolute Deviation (MAD)591
Skewness-0.14883725
Sum11775818
Variance664204.36
MonotonicityNot monotonic
2023-12-26T10:01:48.615503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
2517 508
 
5.0%
1965 12
 
0.1%
1480 12
 
0.1%
1434 11
 
0.1%
1664 11
 
0.1%
1720 11
 
0.1%
1590 10
 
0.1%
1542 10
 
0.1%
1528 10
 
0.1%
Other values (1964) 7062
69.7%
ValueCountFrequency (%)
0 2470
24.4%
132 1
 
< 0.1%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 2
 
< 0.1%
168 2
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
ValueCountFrequency (%)
2517 508
5.0%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 2
 
< 0.1%
2508 2
 
< 0.1%
2507 4
 
< 0.1%
2506 1
 
< 0.1%
2505 3
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ)

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.1396
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.664161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.6853
Coefficient of variation (CV)1.2170994
Kurtosis1.7986173
Mean7469.1396
Median Absolute Deviation (MAD)2665
Skewness1.6616965
Sum75639977
Variance82640560
MonotonicityNot monotonic
2023-12-26T10:01:48.717773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 324
 
3.2%
34516 98
 
1.0%
31999 26
 
0.3%
787 8
 
0.1%
701 7
 
0.1%
713 7
 
0.1%
953 7
 
0.1%
463 7
 
0.1%
990 6
 
0.1%
788 6
 
0.1%
Other values (6803) 9631
95.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
34516 98
1.0%
34362 1
 
< 0.1%
34302 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34140 1
 
< 0.1%
34119 1
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75994065
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.769948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.21920677
Coefficient of variation (CV)0.28845248
Kurtosis9.9935012
Mean0.75994065
Median Absolute Deviation (MAD)0.114
Skewness1.7320634
Sum7695.919
Variance0.048051608
MonotonicityNot monotonic
2023-12-26T10:01:48.823047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.791 36
 
0.4%
0.712 34
 
0.3%
0.743 34
 
0.3%
0.718 33
 
0.3%
0.735 33
 
0.3%
0.744 32
 
0.3%
0.699 32
 
0.3%
0.722 32
 
0.3%
0.731 31
 
0.3%
0.631 31
 
0.3%
Other values (1148) 9799
96.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.01 1
 
< 0.1%
0.018 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
 
< 0.1%
0.072 1
 
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
ValueCountFrequency (%)
3.397 1
< 0.1%
3.355 1
< 0.1%
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.357 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.275 1
< 0.1%
2.271 1
< 0.1%

Total_Trans_Amt
Real number (ℝ)

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.0863
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.875704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.1293
Coefficient of variation (CV)0.77135847
Kurtosis3.8940234
Mean4404.0863
Median Absolute Deviation (MAD)1308
Skewness2.0410034
Sum44600182
Variance11540487
MonotonicityNot monotonic
2023-12-26T10:01:48.928499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 11
 
0.1%
4509 11
 
0.1%
4518 10
 
0.1%
2229 10
 
0.1%
4220 9
 
0.1%
4869 9
 
0.1%
4037 9
 
0.1%
4313 9
 
0.1%
4498 9
 
0.1%
4042 9
 
0.1%
Other values (5023) 10031
99.1%
ValueCountFrequency (%)
510 1
< 0.1%
530 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
643 1
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Total_Trans_Ct
Real number (ℝ)

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:48.980732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2023-12-26T10:01:49.033098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
71 203
 
2.0%
75 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
74 190
 
1.9%
78 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71222238
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:49.084465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.23808609
Coefficient of variation (CV)0.33428617
Kurtosis15.689293
Mean0.71222238
Median Absolute Deviation (MAD)0.119
Skewness2.0640306
Sum7212.676
Variance0.056684987
MonotonicityNot monotonic
2023-12-26T10:01:49.137380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 171
 
1.7%
1 166
 
1.6%
0.5 161
 
1.6%
0.75 156
 
1.5%
0.6 113
 
1.1%
0.8 101
 
1.0%
0.714 92
 
0.9%
0.833 85
 
0.8%
0.778 69
 
0.7%
0.625 63
 
0.6%
Other values (820) 8950
88.4%
ValueCountFrequency (%)
0 7
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 3
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3.714 1
 
< 0.1%
3.571 1
 
< 0.1%
3.5 1
 
< 0.1%
3.25 1
 
< 0.1%
3 2
< 0.1%
2.875 1
 
< 0.1%
2.75 1
 
< 0.1%
2.571 1
 
< 0.1%
2.5 3
< 0.1%
2.429 1
 
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ)

ZEROS 

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27489355
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:49.191477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.27569147
Coefficient of variation (CV)1.0029026
Kurtosis-0.79497195
Mean0.27489355
Median Absolute Deviation (MAD)0.176
Skewness0.718008
Sum2783.847
Variance0.076005786
MonotonicityNot monotonic
2023-12-26T10:01:49.244547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
0.073 44
 
0.4%
0.057 33
 
0.3%
0.048 32
 
0.3%
0.06 30
 
0.3%
0.061 29
 
0.3%
0.045 29
 
0.3%
0.059 28
 
0.3%
0.069 28
 
0.3%
0.053 27
 
0.3%
Other values (954) 7377
72.8%
ValueCountFrequency (%)
0 2470
24.4%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.006 3
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 4
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%
Distinct1704
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15999746
Minimum7.6642 × 10-6
Maximum0.99958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:49.296457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.6642 × 10-6
5-th percentile4.2358 × 10-5
Q19.8983 × 10-5
median0.00018146
Q30.0003373
95-th percentile0.99697
Maximum0.99958
Range0.99957234
Interquartile range (IQR)0.000238317

Descriptive statistics

Standard deviation0.36530101
Coefficient of variation (CV)2.2831675
Kurtosis1.4175352
Mean0.15999746
Median Absolute Deviation (MAD)0.000110234
Skewness1.8485384
Sum1620.2943
Variance0.13344483
MonotonicityNot monotonic
2023-12-26T10:01:49.348338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00019864 80
 
0.8%
0.0003139 78
 
0.8%
0.00030251 77
 
0.8%
0.00018665 73
 
0.7%
0.00011382 71
 
0.7%
0.00019143 66
 
0.7%
0.00011811 66
 
0.7%
0.00017987 63
 
0.6%
0.00018145 60
 
0.6%
0.00016883 59
 
0.6%
Other values (1694) 9434
93.2%
ValueCountFrequency (%)
7.6642 × 10-61
< 0.1%
7.7559 × 10-61
< 0.1%
1.0252 × 10-51
< 0.1%
1.0546 × 10-51
< 0.1%
1.1536 × 10-51
< 0.1%
1.4461 × 10-51
< 0.1%
1.6948 × 10-51
< 0.1%
1.6949 × 10-51
< 0.1%
1.7434 × 10-52
< 0.1%
1.7785 × 10-51
< 0.1%
ValueCountFrequency (%)
0.99958 3
< 0.1%
0.99954 1
 
< 0.1%
0.99945 3
< 0.1%
0.99944 4
< 0.1%
0.99943 1
 
< 0.1%
0.99942 2
 
< 0.1%
0.99939 6
0.1%
0.99938 5
< 0.1%
0.99937 2
 
< 0.1%
0.99936 1
 
< 0.1%

Education_Level_num
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8657055
Minimum0
Maximum5
Zeros1500
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-12-26T10:01:49.490796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3216381
Coefficient of variation (CV)0.70838514
Kurtosis0.044064117
Mean1.8657055
Median Absolute Deviation (MAD)1
Skewness0.66184702
Sum18894
Variance1.7467272
MonotonicityNot monotonic
2023-12-26T10:01:49.528758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3696
36.5%
1 2602
25.7%
0 1500
14.8%
3 1016
 
10.0%
4 713
 
7.0%
5 600
 
5.9%
ValueCountFrequency (%)
0 1500
14.8%
1 2602
25.7%
2 3696
36.5%
3 1016
 
10.0%
4 713
 
7.0%
5 600
 
5.9%
ValueCountFrequency (%)
5 600
 
5.9%
4 713
 
7.0%
3 1016
 
10.0%
2 3696
36.5%
1 2602
25.7%
0 1500
14.8%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0
3732 
3
2014 
1
1878 
2
1770 
4
733 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 3732
36.9%
3 2014
19.9%
1 1878
18.5%
2 1770
17.5%
4 733
 
7.2%

Length

2023-12-26T10:01:49.569816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:49.606663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3732
36.9%
3 2014
19.9%
1 1878
18.5%
2 1770
17.5%
4 733
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 3732
36.9%
3 2014
19.9%
1 1878
18.5%
2 1770
17.5%
4 733
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3732
36.9%
3 2014
19.9%
1 1878
18.5%
2 1770
17.5%
4 733
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3732
36.9%
3 2014
19.9%
1 1878
18.5%
2 1770
17.5%
4 733
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3732
36.9%
3 2014
19.9%
1 1878
18.5%
2 1770
17.5%
4 733
 
7.2%

Card_Category_num
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0
9436 
3
 
555
1
 
116
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9436
93.2%
3 555
 
5.5%
1 116
 
1.1%
2 20
 
0.2%

Length

2023-12-26T10:01:49.648781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:49.683456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 9436
93.2%
3 555
 
5.5%
1 116
 
1.1%
2 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 9436
93.2%
3 555
 
5.5%
1 116
 
1.1%
2 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9436
93.2%
3 555
 
5.5%
1 116
 
1.1%
2 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9436
93.2%
3 555
 
5.5%
1 116
 
1.1%
2 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9436
93.2%
3 555
 
5.5%
1 116
 
1.1%
2 20
 
0.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
1
5194 
2
4181 
0
752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5194
51.3%
2 4181
41.3%
0 752
 
7.4%

Length

2023-12-26T10:01:49.721875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:49.755930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5194
51.3%
2 4181
41.3%
0 752
 
7.4%

Most occurring characters

ValueCountFrequency (%)
1 5194
51.3%
2 4181
41.3%
0 752
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5194
51.3%
2 4181
41.3%
0 752
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5194
51.3%
2 4181
41.3%
0 752
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5194
51.3%
2 4181
41.3%
0 752
 
7.4%

Marital_Status_Divorced
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0.0
9375 
1.0
 
752

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30381
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9375
92.6%
1.0 752
 
7.4%

Length

2023-12-26T10:01:49.793068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:49.826918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9375
92.6%
1.0 752
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 19502
64.2%
. 10127
33.3%
1 752
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20254
66.7%
Other Punctuation 10127
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19502
96.3%
1 752
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 10127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30381
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19502
64.2%
. 10127
33.3%
1 752
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19502
64.2%
. 10127
33.3%
1 752
 
2.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
1.0
5194 
0.0
4933 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30381
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5194
51.3%
0.0 4933
48.7%

Length

2023-12-26T10:01:49.862095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:49.896062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5194
51.3%
0.0 4933
48.7%

Most occurring characters

ValueCountFrequency (%)
0 15060
49.6%
. 10127
33.3%
1 5194
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20254
66.7%
Other Punctuation 10127
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15060
74.4%
1 5194
 
25.6%
Other Punctuation
ValueCountFrequency (%)
. 10127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30381
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15060
49.6%
. 10127
33.3%
1 5194
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15060
49.6%
. 10127
33.3%
1 5194
 
17.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0.0
5946 
1.0
4181 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30381
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5946
58.7%
1.0 4181
41.3%

Length

2023-12-26T10:01:49.932183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:49.965499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5946
58.7%
1.0 4181
41.3%

Most occurring characters

ValueCountFrequency (%)
0 16073
52.9%
. 10127
33.3%
1 4181
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20254
66.7%
Other Punctuation 10127
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16073
79.4%
1 4181
 
20.6%
Other Punctuation
ValueCountFrequency (%)
. 10127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30381
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16073
52.9%
. 10127
33.3%
1 4181
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16073
52.9%
. 10127
33.3%
1 4181
 
13.8%

Attrition_Num
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0
8500 
1
1627 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Length

2023-12-26T10:01:50.002630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:50.035855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0
5358 
1
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5358
52.9%
1 4769
47.1%

Length

2023-12-26T10:01:50.072990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T10:01:50.106022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5358
52.9%
1 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
0 5358
52.9%
1 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10127
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5358
52.9%
1 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10127
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5358
52.9%
1 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5358
52.9%
1 4769
47.1%

Interactions

2023-12-26T10:01:46.306416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-26T10:01:39.103659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-26T10:01:41.376139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-26T10:01:42.047376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2023-12-26T10:01:43.486496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-26T10:01:44.141232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-26T10:01:44.810272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-26T10:01:45.559169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2023-12-26T10:01:46.265562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2023-12-26T10:01:46.972662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-26T10:01:47.135769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Education_Level_numIncome_Category_numCard_Category_numMarital_Status_numMarital_Status_DivorcedMarital_Status_MarriedMarital_Status_SingleAttrition_NumMale
0768805383Existing Customer45M3High SchoolMarried$60K - $80KBlue3951312691.077711914.01.3351144421.6250.0610.00009312010.01.00.001
1818770008Existing Customer49F5GraduateSingleLess than $40KBlue446128256.08647392.01.5411291333.7140.1050.00005720020.00.01.000
2713982108Existing Customer51M3GraduateMarried$80K - $120KBlue364103418.003418.02.5941887202.3330.0000.00002123010.01.00.001
3769911858Existing Customer40F4High SchoolSingleLess than $40KBlue343413313.02517796.01.4051171202.3330.7600.00013410020.00.01.000
4709106358Existing Customer40M3UneducatedMarried$60K - $80KBlue215104716.004716.02.175816282.5000.0000.00002202010.01.00.001
5713061558Existing Customer44M2GraduateMarried$40K - $60KBlue363124010.012472763.01.3761088240.8460.3110.00005521010.01.00.001
6810347208Existing Customer51M4GraduateMarried$120K +Gold4661334516.0226432252.01.9751330310.7220.0660.00012324110.01.00.001
7818906208Existing Customer32M0High SchoolMarried$60K - $80KSilver2722229081.0139627685.02.2041538360.7140.0480.00008612310.01.00.001
8710930508Existing Customer37M3UneducatedSingle$60K - $80KBlue3652022352.0251719835.03.3551350241.1820.1130.00004502020.00.01.001
9719661558Existing Customer48M2GraduateSingle$80K - $120KBlue3663311656.016779979.01.5241441320.8820.1440.00030323020.00.01.001
CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Education_Level_numIncome_Category_numCard_Category_numMarital_Status_numMarital_Status_DivorcedMarital_Status_MarriedMarital_Status_SingleAttrition_NumMale
10117712503408Existing Customer57M2GraduateMarried$80K - $120KBlue4063417925.0190916016.00.712174981110.8200.1060.00051623010.01.00.001
10118713755458Attrited Customer50M1High SchoolMarried$80K - $120KBlue366349959.09529007.00.82510310631.1000.0960.99813013010.01.00.011
10119716893683Attrited Customer55F3UneducatedSingleLess than $40KBlue4743314657.0251712140.00.1666009530.5140.1720.99691000020.00.01.010
10120710841183Existing Customer54M1High SchoolSingle$60K - $80KBlue3452013940.0210911831.00.660155771140.7540.1510.00003812020.00.01.001
10121713899383Existing Customer56F1GraduateSingleLess than $40KBlue504143688.06063082.00.570145961200.7910.1640.00014820020.00.01.000
10122772366833Existing Customer50M2GraduateSingle$40K - $60KBlue403234003.018512152.00.703154761170.8570.4620.00019121020.00.01.001
10123710638233Attrited Customer41M2High SchoolDivorced$40K - $60KBlue254234277.021862091.00.8048764690.6830.5110.99527011001.00.00.011
10124716506083Attrited Customer44F1High SchoolMarriedLess than $40KBlue365345409.005409.00.81910291600.8180.0000.99788010010.01.00.010
10125717406983Attrited Customer30M2GraduateMarried$40K - $60KBlue364335281.005281.00.5358395620.7220.0000.99671021010.01.00.011
10126714337233Attrited Customer43F2GraduateMarriedLess than $40KSilver2562410388.019618427.00.70310294610.6490.1890.99662020310.01.00.010